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Enregistrement W2555467409 · doi:10.1249/fit.0b013e318201cfb7

Glycemic Index and Athletic Performance

2011· article· en· W2555467409 sur OpenAlexaff
Stella L. Volpe

Notice bibliographique

RevueACSMʼs Health & Fitness Journal · 2011
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueMuscle metabolism and nutrition
Établissements canadiensCanadian Journal of Communication (Canada)The Journal of Student Science and Technology
Organismes subventionnairesnon disponible
Mots-clésGlycemic indexGlycemic loadGlycemicMedicineBlood sugarCarbohydrateFood scienceAnimal scienceInternal medicineDiabetes mellitusEndocrinologyChemistryBiology

Résumé

récupéré en direct d'OpenAlex

The glycemic index (GI) of foods has been a highly discussed topic in the sports nutrition field. Some have recommended that athletes consume low-glycemic index foods before exercise and high-glycemic index foods after exercise; however, the research evidence has provided equivocal results. This Nutritionist’s View article will discuss what the GI is, and some research that has been conducted on its effects on athletic performance. WHAT IS THE GLYCEMIC INDEX? The GI of foods was first established in the early 1980s (5). The GI is the effect that a carbohydrate food has on the blood glucose (blood sugar) levels in the body. A high-glycemic index food will increase the blood glucose levels, whereas low-glycemic index foods have small effects on blood glucose levels. See Table for examples of low-, medium-, and high-glycemic index foods.TABLE: Examples of Low–, Medium–, and High–Glycemic Index FoodsHOW IS THE GLYCEMIC INDEX MEASURED? The GI is measured by giving a fixed amount of food to a person (typically 50 g of a carbohydrate food). Blood is taken at 30 minutes, 60 minutes, 90 minutes, and 2 hours postconsumption. Typically, the 2-hour postconsumption blood glucose is compared with that of a standard (usually pure glucose). The area under the glucose curve is what is measured and that is divided by the standard (pure glucose), then multiplied by 100. For example, a GI of 80% means that eating 50 g of that particular food results in a rise of blood glucose that is 80% as great as consuming 50 g of pure glucose. The lower the GI, the slower rate of digestion and absorption of that food. A higher GI food would indicate a faster rate of digestion and absorption, thus, a quicker rise in blood glucose levels. FACTORS AFFECTING THE GLYCEMIC INDEX Measuring the GI is not as straightforward as it sounds, however. Although it provides a good estimation of how food may be digested and absorbed, other factors come into play, as well. For example, the structure of the carbohydrate, time and consumption of other foods, and other substances within foods (e.g., protein and fat) can affect the GI of foods. In addition, when the GI is tested in a laboratory or hospital, the person is required to be fasted beforehand. However, in a typical day, a person will usually not consume foods separately as is conducted in the laboratory or hospital and will not always be fasted before consuming foods. Thus, the other types of foods and other substances in foods (e.g., protein and fat) can effect (lower or increase) the GI of a food.FigureGLYCEMIC INDEX AND ATHLETIC PERFORMANCE The research that has been performed on GI and athletic performance has provided varying results. For example, Kirwan et al. (2) compared the effects of moderate- and high-glycemic index foods on glucose availability during exercise and exercise performance time. Six male volunteers were asked to consume 75 g of carbohydrate in the form of two different breakfast cereals: 1) rolled oats, moderate GI of 61% versus 2) puffed rice, high GI of 82%; both also were compared with a control (water). This was a crossover design study, and the order of each trial was randomized. The authors measured blood glucose, fatty acids, glycerol, insulin, epinephrine, and norepinephrine concentrations, as well as muscle biopsies before and after the exercise. They reported that the moderate GI breakfast cereal, consumed 45 minutes before exercise, not only improved exercise performance time, but also maintained blood glucose at a normal concentration for a longer time during the exercise bout. They also found that carbohydrate use was greatest during the moderate GI trial. Moore et al. (4) compared low- versus high-glycemic index meals on time trial performance in eight male cyclists. The cyclists were provided the same amount of carbohydrate per trial (1 g kg−1 of body weight), 45 minutes before a 40-km time trial. They found that time trial performance was significantly improved in the low-glycemic index trial compared with the high-glycemic index trial (92.5 ± 5.2 minutes vs. 95.6 ± 6.0 minutes, respectively; P = 0.009). They also reported that blood glucose levels were significantly greater at the time of exhaustion in the low- versus high-glycemic index trial. They did not find differences in carbohydrate or fat oxidation between the low- and high-glycemic index trials. They concluded that “the improvement in time trial performance for the low-glycemic index trial may be associated with an increased availability of glucose to the working muscles, contributing additional carbohydrate for oxidation and possibly sparing limited muscle and liver glycogen stores” (4). In contrast, Stevenson et al. (6) studied nine endurance-trained individuals who first cycled for 90 minutes at 70% of their peak oxygen consumption. After this exercise bout, the participants consumed either high- or low-glycemic index meals during the next 12 hours. On the next day, and after an overnight fast, the participants cycled for 90 minutes again. They reported that the high-glycemic index diet decreased nonesterified fatty acid availability but increased the dependence on muscle triglycerides. They did not find an effect of either low- or high-glycemic index meals on muscle or liver glycogen storage or use. SUMMARY Based on the few studies presented in this Nutritionist’s View, it is clear that the effects of consuming low-, medium-, or high-glycemic index foods do not provide consistent results. A few things to consider about these studies and results are the following: 1) all the trials were conducted in men, 2) longitudinal data of consumption of varying degrees of GI foods should be conducted, 3) hydration status and training status could impact the results, and 4) field trials need to be conducted to evaluate if there are differences between the laboratory setting and field setting. In their review of GI in sport nutrition, Mondazzi and Arcelli (3) state that although at the biochemical level there has been consistent evidence that altering the glycemic index may change fat and carbohydrate oxidation during exercise, less consistent evidence has shown that this resulted in improved athletic performance (e.g., functional level improvement). Perhaps this could be caused by short-term dietary manipulation of the GI. Donaldson et al. (1) concur, “Despite the fact that the relationship between GI and sporting performance has been a topic of research for more than 15 years, there is no consensus on whether consuming CHO (carbohydrate) of differing GI improves endurance performance. Until further well-designed research is carried out, athletes are encouraged to follow standard recommendations for CHO consumption and let practical issues and individual experience dictate the use of HGI or LGI meals and supplements before, during, and after exercise.” My advice is to consume healthier carbohydrates most of the time. Consumption of whole grains and other more complex carbohydrates will not only help exercise performance, but also result in better health.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,718
Score d'incertitude au seuil0,424

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,024
Tête enseignante GPT0,257
Écart entre enseignants0,233 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2011
Routes d'admission1
Résumé présentoui

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